Data driven storage location assignment problem considering order picking frequencies: A heuristic approach

The main variables that influence the efficiency of a warehouse are the use of space and the order picking distance. In the literature, there are proposals to add the costs with space and order picking in order to evaluate each alternative for storage location assignment. However, there were problems with the adoption of this methodology, including difficulties in determining the costs and tradeoffs between them. These difficulties can result in solutions that are suboptimal. Based on these facts, this paper proposes a class-based storage process and storage location assignment by a cube-per-order index (COI) that analyzes the space required and the total order picking distance by Pareto-optimal calculations. The efficient frontier possibilities allow the reduction of the set of alternatives, and the DM can analyze only the alternatives on efficient frontier.

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Defining a storage-assignment strategy for precedence-constrained order picking

Operations Research and Decisions ◽

10.37190/ord210207 ◽

2021 ◽

Vol 31 (2) ◽

Author(s):

Maria A. M. Trindade ◽

Paulo S. A. Sousa ◽

Maria R. A. Moreira

Keyword(s):

Assignment Problem ◽

Real Life ◽

Order Picking ◽

Quadratic Assignment ◽

Assignment Model ◽

Storage Assignment ◽

Storage Location ◽

Assignment Strategy ◽

Location Assignment ◽

The Creation

This paper proposes a zero-one quadratic assignment model for dealing with the storage location assignment problem when there are weight constraints. Our analysis shows that operations can be improved using our model. When comparing the strategy currently used in a real-life company with the designed model, we found that the new placement of products allowed a reduction of up to 22% on the picking distance. This saving is higher than that achieved with the creation of density zones, a procedure commonly used to deal with weight constraints, according to the literature.

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A genetic programming-based hyper-heuristic approach for storage location assignment problem

2014 IEEE Congress on Evolutionary Computation (CEC) ◽

10.1109/cec.2014.6900604 ◽

2014 ◽

Cited By ~ 12

Author(s):

Jing Xie ◽

Yi Mei ◽

Andreas T. Ernst ◽

Xiaodong Li ◽

Andy Song

Keyword(s):

Genetic Programming ◽

Assignment Problem ◽

Heuristic Approach ◽

Storage Location ◽

Location Assignment

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Particle Swarm Optimization Using Neighborhood-Based Mutation Operator and Intermediate Disturbance Strategy for Outbound Container Storage Location Assignment Problem

Mathematical Problems in Engineering ◽

10.1155/2019/9132315 ◽

2019 ◽

Vol 2019 ◽

pp. 1-13 ◽

Cited By ~ 2

Author(s):

Yuyan He ◽

Aihu Wang ◽

Hailiang Su ◽

Mengyao Wang

Keyword(s):

Particle Swarm Optimization ◽

Assignment Problem ◽

Particle Swarm ◽

Local Solution ◽

Mutation Operator ◽

Swarm Optimization ◽

Intermediate Disturbance ◽

Storage Location ◽

Location Assignment ◽

Container Storage Location Assignment

Outbound container storage location assignment problem (OCSLAP) could be defined as how a series of outbound containers should be stacked in the yard according to certain assignment rules so that the outbound process could be facilitated. Considering the NP-hard nature of OCSLAP, a novel particle swarm optimization (PSO) method is proposed. The contributions of this paper could be outlined as follows: First, a neighborhood-based mutation operator is introduced to enrich the diversity of the population to strengthen the exploitation ability of the proposed algorithm. Second, a mechanism to transform the infeasible solutions into feasible ones through the lowest stack principle is proposed. Then, in the case of trapping into the local solution in the search process, an intermediate disturbance strategy is implemented to quickly jump out of the local solution, thereby enhancing the global search capability. Finally, numerical experiments have been done and the results indicate that the proposed algorithm achieves a better performance in solving OCSLAP.

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